머신러닝을 이용한 에너지 선택적 유방촬영의 진단 정확도 향상에 관한 연구

• Published in: 방사선기술과학 Vol. 42; no. 1; pp. 9 - 17
• Main Authors: 엄지수(Ji soo Eom), 이승완(Seung wan Lee), 김번영(Burn young Kim)
• Format: Journal Article
• Language: Korean
• Published: 대한방사선과학회(구 대한방사선기술학회) 28.02.2019; KOREAN SOCIETY OF RADIOLOGICAL TECHNOLOGY; 대한방사선과학회
• Subjects: 방사선과학; 이중에너지; 머신러닝; 디지털 유방촬영; 분류; Dual-energy; Machine learning; Classification; Tumor thickness; 종양 두께; Digital mammography
• ISSN: 2288-3509; 2384-1168
• DOI: 10.17946/JRST.2019.42.1.9

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.